| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import requests | |
| from PIL import Image | |
| from torchvision import transforms | |
| model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval() | |
| # Download human-readable labels for ImageNet. | |
| response = requests.get("https://git.io/JJkYN") | |
| labels = response.text.split("\n") | |
| # def sepia(input_img): | |
| # sepia_filter = np.array([ | |
| # [0.393, 0.769, 0.189], | |
| # [0.349, 0.686, 0.168], | |
| # [0.272, 0.534, 0.131] | |
| # ]) | |
| # sepia_img = input_img.dot(sepia_filter.T) | |
| # sepia_img /= sepia_img.max() | |
| # return sepia_img | |
| # def greet(name): | |
| # return "Hello " + name + "!!" | |
| def predict(inp): | |
| inp = transforms.ToTensor()(inp).unsqueeze(0) | |
| with torch.no_grad(): | |
| prediction = torch.nn.functional.softmax(model(inp)[0], dim=0) | |
| confidences = {labels[i]: float(prediction[i]) for i in range(1000)} | |
| return confidences | |
| # demo = gr.Interface(fn=sepia, inputs="image", outputs="image") | |
| demo = gr.Interface(fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Label(num_top_classes=3), | |
| examples=["lion.jpg", "cheetah.jpg"]) | |
| demo.launch() | |
| # iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| # iface.launch() |